Abstract Renewed interest in the microbiome has yielded numerous intriguing findings about the role of bacterial symbiontsinhumanbiologyanddisease.However,mostofthesefindingsarecorrelativeandassociative;?little is known about microbiota-host interactions at the level of molecular mechanism. Typical approaches to addressthisproblemareone-offeffortsthatattempttofindanindividualmoleculeresponsibleforaphenotype of interest. Here, we propose to upend this paradigm by systematically studying one of the most concrete contributions of the microbiota to human biology: the ?top 100? molecules, by abundance, from the gut community. These molecules vary widely in concentration among individuals, can accumulate in host circulation,andarepresentatlevelsthatmatchorexceedtheconcentrationofatypicalsmallmoleculedrug. Theworkweproposehere?todeterminethebacterialspeciesthatproduceeachofthetop100,andidentify the genes responsible ? will be the first step toward creating a capability to completely specify the molecular outputofthegutcommunity(whichmoleculesareproduced,andwhichothersarenot),aprocesswewillpilot inthecurrentproject. Thisensembleofdozensofhigh-concentrationmolecules?towhichweareexposeddaily?islikelytobea majordriverofhumanbiologyanddisease.Itisnotunreasonabletoimagineafutureinwhicheveryhuman will harbor a ?reprogrammed? (synthetic) gut community whose molecular output has been optimized for diseasetreatmentandprevention. The fields of natural product discovery and microbiome research are dominated by genomics-driven approaches.Thesolutionweproposehererunsentirelycountertothistrend:wewillstartwith1)anempirical approachthatis,inessence,old-fashionedBergey?s-stylemicrobiologyoutfittedwithstate-of-the-artanalytical chemistry.Usingtherichinformationwederivefromempiricalmetabolicprofiling,wewillthen2)usegenetics and biochemistry to identify the genes responsible for synthesizing the top 100, 3) use this information to deviseacomputationalalgorithmthatcanpredictmetabolicoutputdirectlyfrommetagenomicsequencedata, and4)testourpredictionsusingsimplesyntheticcommunities.Ourdatawillcreatearichmetabolicmapofthe top 100, and will enable the construction of transplant-ready synthetic communities that produce custom cocktailsofdesiredmolecules(anddonotproduceundesiredmolecules).